Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics: Techniques and Applications

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Acknowledgement
Editors
Contributors
Part I: Machine Learning Techniques in Biomedical and Health Informatics
	Chapter 1: Effect of Socio-economic and Environmental Factors on the Growth Rate of COVID-19 with an Overview of Speech Data for Its Early Diagnosis
		1.1 Introduction
			1.1.1 Motivation and Research Objective
		1.2 Databases and Socioeconomic, Environmental Features
			1.2.1 Temperature (f1)
			1.2.2 Happiness Index (f2)
			1.2.3 Cleanliness Index (f3)
			1.2.4 Gross Domestic Product (f4)
			1.2.5 Pollution Index (f5)
			1.2.6 Number of Caregivers/Nurses per 1000 People (f6)
			1.2.7 Number of Physicians per 1000 People (f7)
			1.2.8 Diabetes Prevalence (f8)
			1.2.9 Population Aged over Sixty-five (f9)
			1.2.10 Smokers above Age Fifteen (f10)
		1.3 Growth Rate Calculation and Feature Selection
			1.3.1 Growth Rate Calculation
			1.3.2 Feature Selection
		1.4 COVID-19 Speech Analysis
		1.5 Conclusion
		References
	Chapter 2: Machine Learning in Healthcare: The Big Picture
		2.1 Overview of Machine Learning
			2.1.1 Why Should Machines Learn?
			2.1.2 What Is Machine Learning?
			2.1.3 Types of Machine Learning
				2.1.3.1 Deductive Learning
				2.1.3.2 Inductive Learning
			2.1.4 Deep Learning
		2.2 Driving Forces for ML in Healthcare
			2.2.1 Big Data in Healthcare
			2.2.2 Demographic Shift
			2.2.3 Global Pandemic
			2.2.4 Pervasive Medical Errors
			2.2.5 Patient-Centric Healthcare
		2.3 Opportunities for ML Applications
			2.3.1 Disease Diagnosis
			2.3.2 Medical Imaging Analysis
			2.3.3 Medical Prognosis
			2.3.4 Smart Record and Personalized Medicine
			2.3.5 Robotic Surgery
			2.3.6 Genomics and Proteomics
			2.3.7 Drug Discovery
			2.3.8 Clinical Trial
			2.3.9 Epidemic Outbreak and Control
		2.4 Key Challenges for ML in Healthcare
			2.4.1 Causality Problem
			2.4.2 Data Limitations
			2.4.3 Model Interpretability Problem
			2.4.4 Adoption Barriers
		2.5 Conclusions
		References
	Chapter 3: Heart Disease Assessment Using Advanced Machine Learning Techniques
		3.1 Introduction
		3.2 Literature Survey
		3.3 Methodology
			3.3.1 Proposed Methods
				3.3.1.1 K-Nearest Neighbors (KNN)
				3.3.1.2 Random Forest Classification
				3.3.1.3 Support Vector Machines
				3.3.1.4 Naive Bayes Classification
				3.3.1.5 Logistic Regression
		3.4 Results
		3.5 Conclusion
		References
	Chapter 4: Classification of Pima Indian Diabetes Dataset Using Support Vector Machine with Polynomial Kernel
		4.1 Introduction
		4.2 Background Details
		4.3 Dataset Description
		4.4 Methodology
			4.4.1 SVM (Support Vector Machine)
			4.4.2 SVM Kernel
				4.4.2.1 Polynomial Kernel Function
					4.4.2.1.1 Homogenous Polynomial Kernel Function
					4.4.2.1.2 Inhomogeneous Polynomial Kernel Function
				4.4.2.2 Gaussian RBF Kernel Function
				4.4.2.3 Sigmoid Kernel Function
				4.4.2.4 Linear Kernel Function
		4.5 Performance Measures
			4.5.1 Classification Accuracy
			4.5.2 Sensitivity
			4.5.3 Specificity
		4.6 Simulation and Experimental Result
			4.6.1 Gain Graph
			4.6.2 Response Graph
		4.7 Conclusion
		4.8 Future Scope
		Reference
	Chapter 5: Analysis and Prediction of COVID-19 Pandemic
		5.1 Introduction
		5.2 Literature Survey
		5.3 Proposed System
		5.4 The Data from the World
		5.5 Results
		5.6 Discussion
		5.7 Conclusion
		References
	Chapter 6: Variational Mode Decomposition Based Automated Diagnosis Method for Epilepsy Using EEG Signals
		6.1 Introduction: Background and Driving Forces
		6.2 Literature Review
			6.2.1 EEG Signal Processing Techniques
			6.2.2 Models and Methods of Classification Used
		6.3 Dataset
		6.4 Variational Mode Decomposition
		6.5 Features
			6.5.1 Second and Fourth Order Difference Plot and Computation of Ellipse Area
		6.6 Features: Renyi Entropy
		6.7 Features Used: Average Amplitude
		6.8 Multilayer Perceptron (MLP) Based Classification
		6.9 Results and Discussion
		6.10 Conclusion
		References
	Chapter 7: Soft-computing Approach in Clinical Decision Support Systems
		7.1 Introduction
		7.2 Literature Review
		7.3 Incorporation of Databases into the CDSS System to Make It More Useful
		7.4 CDSS Alerts, ADEs, and Physician Burnout
		7.5 CDSS Security
		7.6 Conclusion
			7.6.1 Research Methodology
			7.6.2 Findings
		Bibliography
	Chapter 8: A Comparative Performance Assessment of a Set of Adaptive Median Filters for Eliminating Noise from Medical Images
		8.1 Introduction
		8.2 Proposed Modified Circular Adaptive Median Filter
		8.3 Simulation Study
			8.3.1 Performance Indices
			8.3.2 Simulation Results
		8.4 Conclusion
		Acknowledgment
		References
	Chapter 9: Early Prediction of Parkinson’s Disease Using Motor, Non-Motor Features and Machine Learning Techniques
		9.1 Introduction
		9.2 Review of Related Literature
		9.3 Materials and Methods
			9.3.1 Dataset Collection
			9.3.2 Feature Descriptions
			9.3.3 Data Pre-Processing
		9.4 Model Description
		9.5 Result and Discussion
		9.6 Conclusion
		9.7 Future Work
		References
Part II: Deep Learning Techniques in Biomedical and Health Informatics
	Chapter 10: Deep Neural Network for Parkinson Disease Prediction Using SPECT Image
		10.1 Introduction
		10.2 Methodology
			10.2.1 Database
			10.2.2 Image Processing
			10.2.3 Methodology
				10.2.3.1 Convolutional Neural Networks (CNN)
				10.2.3.2 Results and Discussion
				10.2.3.3 Comparison with Related Work
		10.3 Discussion
		10.4 Conclusion
		References
	Chapter 11: An Insight into Applications of Deep Learning in Bioinformatics
		11.1 Introduction
		11.2 Models in Deep Learning
			11.2.1 Convolutional Neural Networks (CNN)
			11.2.2 Recurrent Neural Network (RNN)
			11.2.3 Autoencoder
			11.2.4 Deep Belief Network (DBN)
		11.3 Deep Learning in Bioinformatics
			11.3.1 Deep Learning for Omics
			11.3.2 Deep Learning for Biomedical Imaging
			11.3.3 Deep Learning for Biomedical Signal Processing
			11.3.4 Transfer Learning for Bio Informatics (TL for Bioinformatics)
			11.3.5 Deep Reinforcement Learning for Bioinformatics
			11.3.6 Deep Few Shot Learning for Bioinformatics
			11.3.7 Deep Learning for Public Health
		11.4 Deep Learning in Bioinformatics: Challenges and Limitations
		11.5 Conclusion
		References
	Chapter 12: Classification of Schizophrenia Associated Proteins Using Amino Acid Descriptors and Deep Neural Network
		12.1 Introduction
		12.2 Protein Dataset Preparation
			12.2.1 Protein Sequence Databases
			12.2.2 STRING Network Analysis
			12.2.3 Three Dimensional Structure of DRD2
		12.3 Feature Table Generation
			12.3.1 Amino Acid Composition
			12.3.2 Physicochemical Properties
			12.3.3 Composition Transition Distribution
			12.3.4 FASGAI Vectors
		12.4 Deep Neural Network
		12.5 Conclusion
		References
	Chapter 13: Deep Learning Architectures, Libraries and Frameworks in Healthcare
		13.1 Introduction
		13.2 Deep Learning
			13.2.1 Overview of Deep Learning
			13.2.2 Deep Learning Frameworks and Libraries
				13.2.2.1 Tensorflow
				13.2.2.2 Keras
				13.2.2.3 PyTorch
				13.2.2.4 Caffe
				13.2.2.5 MXNet
				13.2.2.6 Chainer
				13.2.2.7 Deeplearning4J
		13.3 Basic Deep Learning Architectures
			13.3.1 Convolutional Neural Networks
			13.3.2 Recurrent Neural Networks
				13.3.2.1 Long-Short-Term-Memory
				13.3.2.2 Gated Recurrent Unit
			13.3.3 Deep Belief Networks
			13.3.4 Generative Adversarial Networks
			13.3.5 Multilayer Perceptron
			13.3.6 Fully Connected Neural Networks
		13.4 Advanced Deep Learning Architectures
			13.4.1 AlexNet
			13.4.2 VCG Net
			13.4.3 GoogLeNet
				13.4.3.1 ResNet
			13.4.4 Deep Recurrent CNN
			13.4.5 Mask Scoring R-CNN
			13.4.6 Ordered Neurons LSTM
			13.4.7 Spherical CNNs
			13.4.8 ResNeXt
			13.4.9 YOLO
			13.4.10 SegNet
			13.4.11 SqueezeNet
		13.5 Conclusion
		References
	Chapter 14: Designing Low-Cost and Easy-to-Access Skin Cancer Detector Using Neural Network Followed by Deep Learning
		14.1 Introduction
			14.1.1 Local and Offline Deployment
			14.1.2 Eliminating Custom Hardware Requirements
			14.1.3 Diversifying Classification
		14.2 Computer-Aided System for Skin Cancer Diagnosis
		14.3 Proposed Method
			14.3.1 Flow of a CNN Model
			14.3.2 Building CNN
				14.3.2.1 Layer
				14.3.2.2 Layer
				14.3.2.3 Layer
				14.3.2.4 Layer
				14.3.2.5 Layer
			14.3.3 Feature Extraction
			14.3.4 Activation Functions
		14.4 Results and Discussions
			14.4.1 Comparison with Machine Learning Approach
				14.4.1.1 Mean
				14.4.1.2 Area
				14.4.1.3 Border
			14.4.2 Comparison with Other CNN Models
		14.5 Future Scope
		14.6 Conclusion
		References
Part III: Internet of Things (IoT) in Biomedical and Health Informatics
	Chapter 15: Application of Artificial Intelligence in IoT-Based Healthcare Systems
		15.1 Introduction
		15.2 Fuzzy Logic and Fuzzy Models of Health Care
		15.3 Evolutionary Computing of Health Care
		15.4 Artificial Neural Network for Health Care
		15.5 A Probabilistic Model for Health Care
			15.5.1 Risks of Probability Model in Healthcare
		15.6 Big Data in Healthcare
			15.6.1 Applications of Big Data in the Healthcare Sector
		15.7 Data Mining in Healthcare
		15.8 CI Applications in Healthcare
			15.8.1 Increases Patient Engagement and Satisfaction
		15.9 Organization of Deep Learning Applications for IoT in Healthcare
			15.9.1 Internet of Healthy Things
			15.9.2 Medical Diagnosis and Differentiation Applications
				15.9.2.1 Automatic Diagnosis of Heart Disease
				15.9.2.2 Automated EEG Disease Diagnosis
				15.9.2.3 Cerebral Vascular Accidents (CVA) Diagnosis [ 60 ]
				15.9.2.4 Detection of Atrial Fibrillation (AF)
				15.9.2.5 Syndrome Differentiation
				15.9.2.6 Diagnosis and the Treatment for Lung Cancer
				15.9.2.7 Classifying Melanoma Diseases
		15.10 Home-Based and Personal Healthcare
			15.10.1 Disease Prediction Applications
			15.10.2 IoMT Monitoring Solutions
		15.11 Medical Internet of Things
			15.11.1 Analysis of the Physiological Parameters
		15.12 Rehabilitation Systems
		15.13 Skin Pathologies and Dietary Assessment
		15.14 Epidemic Diseases Treatment and Location-Aware Solutions
		References
	Chapter 16: Computational Intelligence in IoT Healthcare
		16.1 Introduction
		16.2 Edge Intelligence in Healthcare System
		16.3 Smart Healthcare Delivery System
		16.4 AI on Edge Architecture in Computational Intelligence for Healthcare System
		16.5 Role of Artificial Intelligence in Diabetes Mellitus Management
		16.6 Role of AI in Cardiovascular Disease Management
		16.7 Role of AI in Neurodegenerative Diseases
		16.8 Challenges of Computational Intelligence in IoT Healthcare
		16.9 Role of AI in Helicobacter Pylori Detection
		16.10 Conclusion and Future Perspectives
		References
	Chapter 17: Machine Learning Techniques for High-Performance Computing for IoT Applications in Healthcare
		17.1 Introduction
		17.2 The application of IoT in the Healthcare System
		17.3 Data in Machine Learning for Healthcare
		17.4 Traditional Centralized Learning: Machine Learning Runs in the Cloud, Gathering Data from Different Hospitals
		17.5 Machine Learning Applications in Disease Prediction
			17.5.1 Cancer
			17.5.2 Diabetes
			17.5.3 Cardiovascular Diseases
			17.5.4 Chronic Kidney Disease
			17.5.5 Parkinson Disease
			17.5.6 Dermatological Diseases
		17.6 Issues and Challenges
		17.7 Conclusions
		References
	Chapter 18: Early Hypertensive Retinopathy Detection Using Improved Clustering Algorithm and Raspberry PI
		18.1 Introduction
		18.2 Preliminaries
			18.2.1 Particle Swarm Optimization Clustering
			18.2.2 Raspberry PI
		18.3 Related Work
		18.4 Methodology
			18.4.1 Pre-processing
			18.4.2 Segmentation
				18.4.2.1 Elevated Continuous Particle Swarm Optimization Clustering
				18.4.2.2 Feature Extraction
		18.5 Experimental Results
			18.5.1 Performance Analysis
		18.6 Conclusion
		References
	Chapter 19: IoT Based Elderly Patient Care System Architecture
		19.1 Introduction
		19.2 Healthcare System Without Patient Mobility Support
		19.3 Healthcare System with Patient Mobility Support
		19.4 Existing Architectures of IoT Based Health Care System
			19.4.1 Comparison of the Existing Health Care Systems
		19.5 Proposed Architecture of IoT Based Elderly Patient Care System
			19.5.1 Features of the Proposed Architecture
			19.5.2 Advantages of the Proposed System
		19.6 Discussion
		19.7 Conclusion
		References
Index